Hadamard Random Forest: Reconstructing real-valued quantum states with exponential reduction in measurement settings

• URL: http://arxiv.org/abs/2505.06455v2
• Date: Fri, 05 Sep 2025 18:08:37 GMT
• Title: Hadamard Random Forest: Reconstructing real-valued quantum states with exponential reduction in measurement settings
• Authors: Zhixin Song, Hang Ren, Melody Lee, Bryan Gard, Nicolas Renaud, Spencer H. Bryngelson,
• Abstract summary: We introduce a readout method for real-valued quantum states that reduces measurement settings required for state vector reconstruction to $O(N_mathrmq)$.<n>We experimentally validate our method up to 10 qubits on the latest available IBM quantum processor and demonstrate that it accurately extracts key properties such as entanglement and magic.
• Score: 1.857570444541311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum tomography is a crucial tool for characterizing quantum states and devices and estimating nonlinear properties of the systems. Performing full quantum state tomography on an $N_\mathrm{q}$ qubit system requires an exponentially increasing overhead with $O(3^{N_\mathrm{q}})$ distinct Pauli measurement settings to resolve all complex phases and reconstruct the density matrix. However, many potential quantum computing applications, such as linear system solves, require only real-valued amplitudes. We introduce a readout method for real-valued quantum states that reduces measurement settings required for state vector reconstruction to $O(N_\mathrm{q})$; the post-processing cost remains exponential $\Omega(2^{N_\mathrm{q}})$. This approach offers a substantial speedup over conventional tomography. We experimentally validate our method up to 10 qubits on the latest available IBM quantum processor and demonstrate that it accurately extracts key properties such as entanglement and magic. Our method also outperforms the standard SWAP test for state overlap estimation. This calculation resembles a numerical integration in certain cases and can be applied to extract nonlinear properties, which are important in application fields. We further implement the method to readout the solution from a quantum linear solver.

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